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应用科学学报 >

2023 , Vol. 41 >Issue 1: 80 - 94

DOI: https://doi.org/10.3969/j.issn.0255-8297.2023.01.007

计算机应用专辑

多层记忆增强生成对抗网络二次预测的视频异常检测方法

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  • 1. 南京师范大学 计算机与电子信息学院/人工智能学院, 江苏 南京 210023;
    2. 南京师范大学 数学科学学院, 江苏 南京 210023

收稿日期: 2022-06-24

  网络出版日期: 2023-02-03

基金资助

国家自然科学基金（No.41971343，No.62102186）资助

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Video Anomaly Detection Method Based on Secondary Prediction of Multi-layer Memory Enhancement Generative Adversarial Network

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  • 1. School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing 210023, Jiangsu, China;
    2. School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, Jiangsu, China

Received date: 2022-06-24

  Online published: 2023-02-03

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摘要

为了提高视频异常检测的准确率，提出了一种基于多层记忆增强生成对抗网络二次预测的视频异常检测方法。首先利用目标检测提取时空立方体，并将其输入自编码器中得到预测帧；其次将预测帧的表观特征和对应真实帧的光流特征进行融合，形成融合特征；最后利用多层记忆增强生成对抗网络二次预测未来帧，以便学习不同层次特征的正常模式并捕获上下文的语义信息。在UCSD Ped2和CUHK Avenue数据集上进行的实验结果表明：所提出的方法与其他视频异常检测方法相比，可有效提高视频异常检测的性能，使帧级别AUC分别达到99.57%和91.59%。

关键词： 视频异常检测; 多层记忆增强; 生成对抗网络; 未来帧预测; 深度学习

本文引用格式

曾静, 李莹, 戚小莎, 吉根林 . 多层记忆增强生成对抗网络二次预测的视频异常检测方法[J]. 应用科学学报, 2023 , 41(1) : 80 -94 . DOI: 10.3969/j.issn.0255-8297.2023.01.007

Abstract

In order to improve the accuracy of video anomaly detection, we propose a video anomaly detection method based on secondary prediction of multi-layer memory enhancement generative adversarial networks. Firstly, a spatiotemporal cube is extracted from target detection, and sent into encoder to obtain a prediction frame. Secondly, the apparent feature of the prediction frame and the optical flow feature of corresponding real frames are fused to form fusion features. Finally, a secondary prediction future frame is generated by using multi-layer memory enhancement generative adversarial networks, for learning normal feature patterns of different levels and capturing the semantic information of context. Experimental results on UCSD Ped2 and CUHK Avenue datasets show that the proposed method can effectively improve the performance of video anomaly detection compared with other video anomaly detection methods, and its frame level AUC reaches 99.57% and 91.59%, respectively.

Key words： video anomaly detection; multi-layer memory enhancement; generative adversarial network; future frame prediction; deep learning

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